Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models"

Related tags

Deep Learning s3m-membership-inference
Overview

Introduction

Official implementation of "Membership Inference Attacks Against Self-supervised Speech Models".

In this work, we demonstrate that existing self-supervised speech model such as HuBERT, wav2vec 2.0, CPC and TERA are vulnerable to membership inference attack (MIA) and thus could reveal sensitive informations related to the training data.

Requirements

  1. Python >= 3.6
  2. Install sox on your OS
  3. Install s3prl on your OS
git clone https://github.com/s3prl/s3prl
cd s3prl
pip install -e ./
  1. Install the specific fairseq
pip install fairseq@git+https://github.com//pytorch/fairseq.git@f2146bdc7abf293186de9449bfa2272775e39e1d#egg=fairseq

Preprocessing

First, extract the self-supervised feature of utterances in each corpus according to your needs.

Currently, only LibriSpeech is available.

BASE_PATH=/path/of/the/corpus
OUTPUT_PATH=/path/to/save/feature
MODEL=wav2vec2
SPLIT=train-clean-100 # you should extract train-clean-100, dev-clean, dev-other, test-clean, test-other

python preprocess_feature_LibriSpeech.py \
    --base_path $BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --split $SPLIT

Speaker-level MIA

After extracting the features, you can apply the attack against the models using either basic attack and improved attack.

Noted that you should run the basic attack to generate the .csv file with similarity scores before performing improved attack.

Basic Attack

SEEN_BASE_PATH=/path/you/save/feature/of/seen/corpus
UNSEEN_BASE_PATH=/path/you/save/feature/of/unseen/corpus
OUTPUT_PATH=/path/to/output/results
MODEL=wav2vec2

python predefined-speaker-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \

Improved Attack

python train-speaker-level-similarity-model.py \
    --seen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --speaker_list "${OUTPUT_PATH}/${MODEL}-customized-speaker-level-attack-similarity.csv"

python customized-speaker-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --similarity_model_path "${OUTPUT_PATH}/customized-speaker-similarity-model-${MODEL}.pt"

Utterance-level MIA

The process for utterance-level MIA is similar to that of speaker-level:

Basic Attack

SEEN_BASE_PATH=/path/you/save/feature/of/seen/corpus
UNSEEN_BASE_PATH=/path/you/save/feature/of/unseen/corpus
OUTPUT_PATH=/path/to/output/results
MODEL=wav2vec2

python predefined-utterance-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \

Improved Attack

python train-utterance-level-similarity-model.py \
    --seen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --speaker_list "${OUTPUT_PATH}/${MODEL}-customized-utterance-level-attack-similarity.csv"

python customized-utterance-level-MIA.py \
    --seen_base_path $SEEN_BATH_PATH \
    --unseen_base_path $UNSEEN_BATH_PATH \
    --output_path $OUTPUT_PATH \
    --model $MODEL \
    --similarity_model_path "${OUTPUT_PATH}/customized-utterance-similarity-model-${MODEL}.pt"

Citation

If you find our work useful, please cite:

You might also like...
Official implementation of Self-supervised Graph Attention Networks (SuperGAT), ICLR 2021.

SuperGAT Official implementation of Self-supervised Graph Attention Networks (SuperGAT). This model is presented at How to Find Your Friendly Neighbor

Dongkwan Kim 127 Dec 28, 2022
An official implementation of
An official implementation of "SFNet: Learning Object-aware Semantic Correspondence" (CVPR 2019, TPAMI 2020) in PyTorch.

PyTorch implementation of SFNet This is the implementation of the paper "SFNet: Learning Object-aware Semantic Correspondence". For more information,

CV Lab @ Yonsei University 87 Dec 30, 2022
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.
This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BiPointNet: Binary Neural Network for Point Clouds Created by Haotong Qin, Zhongang Cai, Mingyuan Zhang, Yifu Ding, Haiyu Zhao, Shuai Yi, Xianglong Li

Haotong Qin 59 Dec 17, 2022
Official code implementation for
Official code implementation for "Personalized Federated Learning using Hypernetworks"

Personalized Federated Learning using Hypernetworks This is an official implementation of Personalized Federated Learning using Hypernetworks paper. [

Aviv Shamsian 121 Dec 25, 2022
StyleGAN2 - Official TensorFlow Implementation
StyleGAN2 - Official TensorFlow Implementation

StyleGAN2 - Official TensorFlow Implementation

NVIDIA Research Projects 10.1k Dec 28, 2022
Old Photo Restoration (Official PyTorch Implementation)
Old Photo Restoration (Official PyTorch Implementation)

Bringing Old Photo Back to Life (CVPR 2020 oral)

Microsoft 11.3k Dec 30, 2022
Official implementation of
Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)

GS-WGAN This repository contains the implementation for GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators (NeurIPS

null 46 Nov 9, 2022
Official PyTorch implementation of Spatial Dependency Networks.
Official PyTorch implementation of Spatial Dependency Networks.

Spatial Dependency Networks: Neural Layers for Improved Generative Image Modeling Đorđe Miladinović   Aleksandar Stanić   Stefan Bauer   Jürgen Schmid

Djordje Miladinovic 34 Jan 19, 2022
Official implementation of YOGO for Point-Cloud Processing
Official implementation of YOGO for Point-Cloud Processing

You Only Group Once: Efficient Point-Cloud Processing with Token Representation and Relation Inference Module By Chenfeng Xu, Bohan Zhai, Bichen Wu, T

Chenfeng Xu 67 Dec 20, 2022
Owner
Wei-Cheng Tseng
Wei-Cheng Tseng
GitHub
Official PyTorch implementation for paper Context Matters: Graph-based Self-supervised Representation Learning for Medical Images

Context Matters: Graph-based Self-supervised Representation Learning for Medical Images Official PyTorch implementation for paper Context Matters: Gra

null 49 Nov 23, 2022
The official implementation of NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021]. https://arxiv.org/pdf/2101.12378.pdf

NeMo: Neural Mesh Models of Contrastive Features for Robust 3D Pose Estimation [ICLR-2021] Release Notes The offical PyTorch implementation of NeMo, p

Angtian Wang 76 Nov 23, 2022
StyleGAN2-ADA - Official PyTorch implementation

Abstract: Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes.

NVIDIA Research Projects 3.2k Dec 30, 2022
Official implementation of the ICLR 2021 paper

You Only Need Adversarial Supervision for Semantic Image Synthesis Official PyTorch implementation of the ICLR 2021 paper "You Only Need Adversarial S

Bosch Research 272 Dec 28, 2022
Official PyTorch implementation of Joint Object Detection and Multi-Object Tracking with Graph Neural Networks

This is the official PyTorch implementation of our paper: "Joint Object Detection and Multi-Object Tracking with Graph Neural Networks". Our project website and video demos are here.

Richard Wang 443 Dec 6, 2022
Official implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis https://arxiv.org/abs/2011.13775

CIPS -- Official Pytorch Implementation of the paper Image Generators with Conditionally-Independent Pixel Synthesis Requirements pip install -r requi

Multimodal Lab @ Samsung AI Center Moscow 201 Dec 21, 2022
Official pytorch implementation of paper "Image-to-image Translation via Hierarchical Style Disentanglement".

HiSD: Image-to-image Translation via Hierarchical Style Disentanglement Official pytorch implementation of paper "Image-to-image Translation

null 364 Dec 14, 2022
Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

IC-Conv This repository is an official implementation of the paper Inception Convolution with Efficient Dilation Search. Getting Started Download Imag

Jie Liu 111 Dec 31, 2022
Official PyTorch Implementation of Unsupervised Learning of Scene Flow Estimation Fusing with Local Rigidity

UnRigidFlow This is the official PyTorch implementation of UnRigidFlow (IJCAI2019). Here are two sample results (~10MB gif for each) of our unsupervis

Liang Liu 28 Nov 16, 2022
Official implementation of our paper "LLA: Loss-aware Label Assignment for Dense Pedestrian Detection" in Pytorch.

LLA: Loss-aware Label Assignment for Dense Pedestrian Detection This project provides an implementation for "LLA: Loss-aware Label Assignment for Dens

null 35 Dec 6, 2022